fix: stop folding global scale into float8 block scales
The fold block_sf (float8) * global_sf (float32) -> float8 loses ~25% precision. Product of ~56-448 block_sf * ~4.65e-05 global_sf lands in float8 low-precision zone where step size is 25%. This makes model output garbage despite finite values. Fix: keep block scales as original float8, return global scales separately as float32 per-expert vectors. Apply global scale as per-expert GEMM alpha in cutlass_grouped_nvfp4_gemm (already iterates per-expert). For L1 with separate gate/up global scales, use gate_gs as alpha and apply up_correction ratio to the up half post-GEMM. weight_transform.py: no more _fold_global_scale, returns (w, sf, global_sf) nvfp4_mega_moe.py: per-expert alpha = activation_gs * weight_gs kernel.py: per_expert_alpha parameter in grouped GEMM deepseek_v4.py: updated type hints and comments
This commit is contained in:
279
diag_b200.py
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279
diag_b200.py
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"""
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NVFP4 MegaMoE Diagnostic — B200
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Checks:
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1. weight_scale_2 values (are they nonzero / loaded correctly?)
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2. Folded scale ranges (clamp/precision loss)
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3. L2 weight/SF orientation sanity
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4. Dequant reference vs CUTLASS output comparison
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5. Single-expert, single-layer test
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"""
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import torch
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import sys
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import os
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import json
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from pathlib import Path
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MODEL_PATH = "/model" # inside the container
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def inspect_checkpoint_scales():
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"""Check raw checkpoint weight_scale_2 values."""
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from safetensors import safe_open
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import glob
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print("=" * 60)
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print("CHECK 1: Checkpoint weight_scale_2 Values")
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print("=" * 60)
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# Find checkpoint files
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ckpt_files = sorted(glob.glob(os.path.join(MODEL_PATH, "*.safetensors")))
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print(f"Found {len(ckpt_files)} safetensors files")
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# Look for expert weight_scale_2 params
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w13_gs_found = 0
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w2_gs_found = 0
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w13_gs_values = {}
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w2_gs_values = {}
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for f in ckpt_files:
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with safe_open(f, framework="pt") as st:
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for key in st.keys():
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if "weight_scale_2" in key and ("experts" in key or "ffn" in key):
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val = st.get_tensor(key)
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if "w13" in key or "gate_up" in key or "w1" in key or "w3" in key:
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w13_gs_found += 1
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if w13_gs_found <= 3:
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w13_gs_values[key] = {"shape": list(val.shape), "dtype": str(val.dtype),
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"min": val.float().min().item(), "max": val.float().max().item(),
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"mean": val.float().mean().item()}
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elif "w2" in key or "down" in key:
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w2_gs_found += 1
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if w2_gs_found <= 3:
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w2_gs_values[key] = {"shape": list(val.shape), "dtype": str(val.dtype),
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"min": val.float().min().item(), "max": val.float().max().item(),
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"mean": val.float().mean().item()}
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print(f"w13 weight_scale_2 entries: {w13_gs_found}")
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print(f"w2 weight_scale_2 entries: {w2_gs_found}")
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for k, v in w13_gs_values.items():
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print(f" {k}: {v}")
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for k, v in w2_gs_values.items():
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print(f" {k}: {v}")
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return w13_gs_found > 0 and w2_gs_found > 0
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def inspect_loaded_model():
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"""Check the model's weight_scale_2 after loading (before finalize_weights)."""
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print("\n" + "=" * 60)
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print("CHECK 2: Model weight_scale_2 After Loading")
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print("=" * 60)
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# We need to load the model and inspect before finalize_weights nukes the params
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# The vLLM server is already running, so let's check the live model
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# Actually, let's load a fresh model instance for inspection
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# Simpler approach: just check the checkpoint directly for scale_2
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# The real check is whether finalize_weights gets called with nonzero scale_2
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print(" (Checkpoint inspection is more reliable — see CHECK 1)")
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print(" The [SF-DEBUG] prints from weight_transform.py should also show this")
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def check_fold_precision_real():
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"""Check float8 folding precision with real checkpoint scales."""
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print("\n" + "=" * 60)
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print("CHECK 3: Float8 Folding Precision (Real Scales)")
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print("=" * 60)
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from safetensors import safe_open
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import glob
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ckpt_files = sorted(glob.glob(os.path.join(MODEL_PATH, "*.safetensors")))
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# Find one layer's expert scales
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for f in ckpt_files:
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with safe_open(f, framework="pt") as st:
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keys = list(st.keys())
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# Find w2 weight_scale and weight_scale_2 for layer 0
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w2_sf_key = None
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w2_gs_key = None
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w13_sf_key = None
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w13_gs_key = None
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for k in keys:
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if "layers.0" in k:
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if "w2" in k and k.endswith("weight_scale") and "scale_2" not in k:
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w2_sf_key = k
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elif "w2" in k and "weight_scale_2" in k:
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w2_gs_key = k
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elif ("w13" in k or "gate_up" in k) and k.endswith("weight_scale") and "scale_2" not in k:
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w13_sf_key = k
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elif ("w13" in k or "gate_up" in k) and "weight_scale_2" in k:
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w13_gs_key = k
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if w2_sf_key and w2_gs_key:
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w2_sf = st.get_tensor(w2_sf_key)
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w2_gs = st.get_tensor(w2_gs_key)
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print(f" L2 block scale: shape={list(w2_sf.shape)} dtype={w2_sf.dtype} "
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f"range=[{w2_sf.float().min():.4e}, {w2_sf.float().max():.4e}]")
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print(f" L2 global scale: shape={list(w2_gs.shape)} dtype={w2_gs.dtype} "
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f"range=[{w2_gs.float().min():.4e}, {w2_gs.float().max():.4e}]")
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# Fold and check precision
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sf_f32 = w2_sf.float()
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gs_f32 = w2_gs.float()
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# Reshape gs for broadcast
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while gs_f32.dim() < sf_f32.dim():
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gs_f32 = gs_f32.unsqueeze(-1)
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product = sf_f32 * gs_f32
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product_clamped = product.clamp(0.0, 448.0)
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folded_f8 = product_clamped.to(torch.float8_e4m3fn)
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folded_back = folded_f8.float()
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# Stats
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n_clamped = (product > 448.0).sum().item()
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n_total = product.numel()
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n_zeroed = (folded_back == 0.0).sum().item()
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rel_err = (folded_back - product).abs() / product.clamp(min=1e-10)
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print(f"\n L2 Fold results:")
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print(f" Clamped to 448: {n_clamped}/{n_total} ({100*n_clamped/n_total:.1f}%)")
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print(f" Zeroed (subnormal): {n_zeroed}/{n_total} ({100*n_zeroed/n_total:.1f}%)")
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print(f" Rel error: max={rel_err.max():.4f} mean={rel_err.mean():.4f} p99={rel_err.quantile(0.99):.4f}")
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# Show distribution of folded values
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fb_hist = torch.histc(folded_back, bins=10, min=0, max=448)
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print(f" Folded value histogram (0-448, 10 bins): {fb_hist.int().tolist()}")
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# CRITICAL CHECK: is the product range within float8?
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print(f" Product range: [{product.min():.4e}, {product.max():.4e}]")
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if n_clamped > 0:
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print(f" ⚠️ {n_clamped} values clamped — this IS precision loss!")
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if w13_sf_key and w13_gs_key:
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w13_sf = st.get_tensor(w13_sf_key)
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w13_gs = st.get_tensor(w13_gs_key)
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print(f"\n L1 block scale: shape={list(w13_sf.shape)} dtype={w13_sf.dtype} "
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f"range=[{w13_sf.float().min():.4e}, {w13_sf.float().max():.4e}]")
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print(f" L1 global scale: shape={list(w13_gs.shape)} dtype={w13_gs.dtype} "
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f"range=[{w13_gs.float().min():.4e}, {w13_gs.float().max():.4e}]")
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break # Just check one file that has layer 0
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def check_l2_weight_semantics():
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"""Verify L2 weight layout by dequantizing and checking against reference."""
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print("\n" + "=" * 60)
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print("CHECK 4: L2 Weight Dequantization Sanity")
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print("=" * 60)
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from safetensors import safe_open
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import glob
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ckpt_files = sorted(glob.glob(os.path.join(MODEL_PATH, "*.safetensors")))
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for f in ckpt_files:
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with safe_open(f, framework="pt") as st:
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keys = list(st.keys())
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# Find layer 0 w2 weight, weight_scale, weight_scale_2
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w2_w = w2_sf = w2_gs = None
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for k in keys:
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if "layers.0" in k:
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if "w2" in k and k.endswith(".weight") and "scale" not in k:
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w2_w = st.get_tensor(k)
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elif "w2" in k and "weight_scale" == k.split(".")[-1]:
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w2_sf = st.get_tensor(k)
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elif "w2" in k and "weight_scale_2" in k:
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w2_gs = st.get_tensor(k)
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if w2_w is not None and w2_sf is not None and w2_gs is not None:
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print(f" w2_weight: shape={list(w2_w.shape)} dtype={w2_w.dtype}")
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print(f" w2_weight_scale: shape={list(w2_sf.shape)} dtype={w2_sf.dtype}")
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print(f" w2_weight_scale_2: shape={list(w2_gs.shape)} dtype={w2_gs.dtype}")
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# Dequantize a small patch
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# w2 is down_proj: (hidden, intermediate) in BF16, or (hidden, inter//2) uint8 for NVFP4
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if w2_w.dtype == torch.uint8:
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# Unpack E2M1
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FP4_LUT = torch.tensor([0, 0.5, 1, 1.5, 2, 3, 4, 6,
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-0, -0.5, -1, -1.5, -2, -3, -4, -6],
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dtype=torch.float32, device=w2_w.device)
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lower = FP4_LUT[(w2_w[:4, :8] & 0x0F).long()]
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upper = FP4_LUT[((w2_w[:4, :8] >> 4) & 0x0F).long()]
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unpacked = torch.empty(4, 16, dtype=torch.float32)
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unpacked[:, 0::2] = lower
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unpacked[:, 1::2] = upper
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# Apply scales
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sf_slice = w2_sf[:4, :1].float() # (4, 1)
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gs = w2_gs.float()
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print(f" Dequantized w2[:4, :16] with sf[:4,:1]={sf_slice.flatten().tolist()}")
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print(f" global_scale_2 = {gs.item() if gs.numel() == 1 else gs[:4].flatten().tolist()}")
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dequant = unpacked * sf_slice * gs.float()
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print(f" Dequantized range: [{dequant.min():.4f}, {dequant.max():.4f}]")
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print(f" Dequantized[:2, :8]: {dequant[:2, :8].tolist()}")
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else:
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print(f" w2_weight is {w2_w.dtype}, not uint8 — may be BF16 checkpoint")
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print(f" w2[:4, :8] = {w2_w[:4, :8].tolist()}")
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break
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def check_ep_reduce_contract():
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"""Verify the EP all-reduce contract with a synthetic test."""
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print("\n" + "=" * 60)
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print("CHECK 5: EP Reduce Contract (Synthetic)")
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print("=" * 60)
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# Simulate 2 ranks
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M, HIDDEN = 4, 8
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# Rank 0: experts 0,1 — tokens routed to expert 0 (slot_weight=0.7) and 1 (slot_weight=0.3)
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y0 = torch.zeros(M, HIDDEN, dtype=torch.bfloat16)
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slot_token_0 = torch.tensor([0, 0, 1, 2, 3]) # which tokens
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slot_weight_0 = torch.tensor([0.7, 0.3, 0.5, 0.6, 0.4], dtype=torch.bfloat16)
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l2_slots_0 = torch.randn(5, HIDDEN, dtype=torch.bfloat16)
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y0.index_add_(0, slot_token_0, l2_slots_0 * slot_weight_0.unsqueeze(1))
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# Rank 1: experts 2,3 — token 0 also routed to expert 2
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y1 = torch.zeros(M, HIDDEN, dtype=torch.bfloat16)
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slot_token_1 = torch.tensor([0, 1])
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slot_weight_1 = torch.tensor([0.2, 0.5], dtype=torch.bfloat16)
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l2_slots_1 = torch.randn(2, HIDDEN, dtype=torch.bfloat16)
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y1.index_add_(0, slot_token_1, l2_slots_1 * slot_weight_1.unsqueeze(1))
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# All-reduce (sum)
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y_final = y0 + y1 # simulated all-reduce
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# Verify: token 0 should have contributions from rank0 (experts 0,1) and rank1 (expert 2)
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expected_0 = (0.7 * l2_slots_0[0] + 0.3 * l2_slots_0[1] + 0.2 * l2_slots_1[0]).bfloat16()
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actual_0 = y_final[0].bfloat16()
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diff = (expected_0 - actual_0).abs().max().item()
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print(f" Token 0: expected vs actual diff = {diff:.6f} ✓" if diff < 0.01 else f" Token 0: MISMATCH diff = {diff}")
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print(f" EP reduce contract is correct — sum of partial rank outputs gives full result")
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if __name__ == "__main__":
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print("NVFP4 MegaMoE Diagnostic — B200")
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print(f"PyTorch: {torch.__version__}, CUDA: {torch.cuda.is_available()}")
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print(f"GPUs: {torch.cuda.device_count()}")
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print()
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try:
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inspect_checkpoint_scales()
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except Exception as e:
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print(f"CHECK 1 FAILED: {e}")
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try:
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check_fold_precision_real()
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except Exception as e:
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print(f"CHECK 3 FAILED: {e}")
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try:
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check_l2_weight_semantics()
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except Exception as e:
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print(f"CHECK 4 FAILED: {e}")
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try:
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check_ep_reduce_contract()
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except Exception as e:
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print(f"CHECK 5 FAILED: {e}")
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66
diag_fold.py
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66
diag_fold.py
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@@ -0,0 +1,66 @@
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"""
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Diagnostic: Check global scale folding precision for NVFP4 weights.
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The fold is: sf_f32 * gs → clamp(0, 448) → float8_e4m3fn
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Question: how much precision is lost in the float8 round-trip?
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"""
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import torch
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# Simulate typical NVFP4 scale distributions
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# block_scale (float8_e4m3fn) range: roughly 0.06 to 448
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# global_scale (float32) range: varies per expert
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# Test 1: If global_scale >> 1, product can exceed 448 → clamp → loss
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# Test 2: If global_scale << 1, product can go subnormal → loss
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# Test 3: Quantization error from 3-bit mantissa
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# Simulate a range of scale values
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block_scales = torch.tensor([0.0625, 0.125, 0.25, 0.5, 1.0, 2.0, 4.0, 8.0, 16.0, 32.0, 64.0, 128.0, 256.0, 448.0], dtype=torch.float32)
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global_scales = torch.tensor([0.001, 0.01, 0.1, 1.0, 10.0, 100.0, 1000.0], dtype=torch.float32)
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print("=== Float8 Folding Precision Analysis ===\n")
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print(f"block_scales: {block_scales.tolist()}")
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print(f"global_scales: {global_scales.tolist()}\n")
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total_clamped = 0
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total_subnormal = 0
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max_rel_error = 0.0
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for gs in global_scales:
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products = block_scales * gs
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clamped = products.clamp(0.0, 448.0)
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folded_f8 = clamped.to(torch.float8_e4m3fn)
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roundtrip = folded_f8.to(torch.float32)
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n_clamped = (products > 448.0).sum().item()
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n_subnormal = (roundtrip > 0).logical_and(roundtrip < 0.0625).sum().item() # rough check
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rel_errors = torch.where(roundtrip > 0, (roundtrip - clamped).abs() / clamped.clamp(min=1e-10), torch.zeros_like(clamped))
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max_err = rel_errors.max().item()
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total_clamped += n_clamped
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total_subnormal += n_subnormal
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max_rel_error = max(max_rel_error, max_err)
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if n_clamped > 0 or max_err > 0.05:
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print(f"gs={gs:.3f}: {n_clamped} clamped, max_rel_err={max_err:.4f}")
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for i, (p, c, r) in enumerate(zip(products, clamped, roundtrip)):
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if abs(r - c) / max(abs(c), 1e-10) > 0.01:
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print(f" block={block_scales[i]:.4f} product={p:.4f} clamped={c:.4f} roundtrip={r:.4f} err={abs(r-c)/max(abs(c),1e-10):.4f}")
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print(f"\nTotal clamped: {total_clamped}, Total subnormal: {total_subnormal}, Max relative error: {max_rel_error:.4f}")
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# The real check: what's the float8_e4m3fn step size at various magnitudes?
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print("\n=== Float8 E4M3 Step Sizes ===")
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test_vals = [0.01, 0.1, 1.0, 10.0, 100.0, 448.0]
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for v in test_vals:
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f8 = torch.tensor(v, dtype=torch.float32).to(torch.float8_e4m3fn)
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back = f8.to(torch.float32)
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# Find next representable value
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u8 = f8.view(torch.uint8)
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next_u8 = u8 + 1
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next_f8 = next_u8.view(torch.float8_e4m3fn)
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next_val = next_f8.to(torch.float32)
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step = next_val - back
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rel_step = step / back if back > 0 else 0
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print(f" value={v:.3f} → f8={back:.6f} → next={next_val:.6f} step={step:.6f} rel={rel_step:.4f}")
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96
diag_fold_real.py
Normal file
96
diag_fold_real.py
Normal file
@@ -0,0 +1,96 @@
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"""
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Critical check: weight_scale_2 values are ~4.65e-05 (TINY).
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When folded: block_sf * 4.65e-05 → most products near zero → float8 can't represent
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This is likely THE bug: folding a float8 scale by a tiny global scale produces
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subnormal/zero values in float8.
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"""
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from safetensors import safe_open
|
||||
import glob
|
||||
import os
|
||||
import torch
|
||||
|
||||
MODEL_PATH = "/model"
|
||||
ckpt_files = sorted(glob.glob(os.path.join(MODEL_PATH, "*.safetensors")))
|
||||
|
||||
# Get layer 0, expert 0 scales
|
||||
for f in ckpt_files:
|
||||
with safe_open(f, framework="pt") as st:
|
||||
keys = list(st.keys())
|
||||
if any("layers.0.mlp.experts.0.gate_proj.weight_scale" in k for k in keys):
|
||||
# Gate
|
||||
gate_sf = st.get_tensor("model.layers.0.mlp.experts.0.gate_proj.weight_scale")
|
||||
gate_gs = st.get_tensor("model.layers.0.mlp.experts.0.gate_proj.weight_scale_2")
|
||||
# Up
|
||||
up_sf = st.get_tensor("model.layers.0.mlp.experts.0.up_proj.weight_scale")
|
||||
up_gs = st.get_tensor("model.layers.0.mlp.experts.0.up_proj.weight_scale_2")
|
||||
# Down
|
||||
down_sf = st.get_tensor("model.layers.0.mlp.experts.0.down_proj.weight_scale")
|
||||
down_gs = st.get_tensor("model.layers.0.mlp.experts.0.down_proj.weight_scale_2")
|
||||
|
||||
print("=" * 60)
|
||||
print("LAYER 0, EXPERT 0 — Scale Analysis")
|
||||
print("=" * 60)
|
||||
|
||||
for name, sf, gs in [("gate", gate_sf, gate_gs), ("up", up_sf, up_gs), ("down", down_sf, down_gs)]:
|
||||
sf_f32 = sf.float()
|
||||
gs_f32 = gs.float()
|
||||
product = sf_f32 * gs_f32
|
||||
product_clamped = product.clamp(0.0, 448.0)
|
||||
folded_f8 = product_clamped.to(torch.float8_e4m3fn)
|
||||
folded_back = folded_f8.float()
|
||||
|
||||
n_total = product.numel()
|
||||
n_clamped = (product > 448.0).sum().item()
|
||||
n_zeroed = (folded_back == 0.0).sum().item()
|
||||
n_nonzero_orig = (sf_f32 > 0).sum().item()
|
||||
n_nonzero_folded = (folded_back > 0).sum().item()
|
||||
|
||||
rel_err = (folded_back - product).abs() / product.clamp(min=1e-10)
|
||||
|
||||
print(f"\n {name}_proj:")
|
||||
print(f" block_sf: shape={list(sf.shape)} range=[{sf_f32.min():.4e}, {sf_f32.max():.4e}] unique_u8={torch.unique(sf.view(torch.uint8)).numel()}")
|
||||
print(f" global_sf: {gs_f32.item():.6e}")
|
||||
print(f" product (sf*gs): range=[{product.min():.4e}, {product.max():.4e}]")
|
||||
print(f" folded (float8): range=[{folded_back.min():.4e}, {folded_back.max():.4e}]")
|
||||
print(f" Clamped to 448: {n_clamped}/{n_total} ({100*n_clamped/n_total:.1f}%)")
|
||||
print(f" Became zero: {n_zeroed}/{n_total} ({100*n_zeroed/n_total:.1f}%)")
|
||||
print(f" Was nonzero → became zero: {n_nonzero_orig - n_nonzero_folded}/{n_nonzero_orig}")
|
||||
print(f" Rel error: max={rel_err.max():.4f} mean={rel_err.mean():.4f}")
|
||||
|
||||
# Show the float8 step size at the product magnitude
|
||||
if product.max() > 0:
|
||||
typical = product.median().item()
|
||||
if typical > 0:
|
||||
f8_typ = torch.tensor(typical, dtype=torch.float32).to(torch.float8_e4m3fn)
|
||||
f8_back = f8_typ.float()
|
||||
if f8_back > 0:
|
||||
step = (f8_typ.view(torch.uint8) + 1).view(torch.float8_e4m3fn).float() - f8_back
|
||||
print(f" Float8 step at median ({typical:.4e}): Δ={step.item():.4e} rel={step.item()/f8_back.item():.2%}")
|
||||
|
||||
break
|
||||
|
||||
# Now check: what if we DON'T fold, and instead pass global_scale as GEMM alpha?
|
||||
print("\n" + "=" * 60)
|
||||
print("ALTERNATIVE: Pass global_scale as GEMM alpha")
|
||||
print("=" * 60)
|
||||
print("""
|
||||
The fold is lossy because float8 can't represent the product range.
|
||||
But if we DON'T fold, the CUTLASS GEMM needs a separate global scale mechanism.
|
||||
|
||||
Option 1: Multiply the GEMM alpha by the weight's global_scale
|
||||
- alpha already carries the activation global scale
|
||||
- We could fold weight global scale into alpha: alpha_new = alpha * weight_gs
|
||||
- BUT: alpha is a single scalar, weight_gs varies per-expert
|
||||
- For grouped GEMM, each expert needs its own alpha
|
||||
|
||||
Option 2: Keep block scales as-is (no fold), multiply output by global_scale
|
||||
- After GEMM: output *= weight_global_scale
|
||||
- This is exact (float32 multiply on bf16 output)
|
||||
- Requires passing global_scale to nvfp4_mega_moe_full
|
||||
|
||||
Option 3: Fold global_scale into the GEMM alpha per-expert
|
||||
- In cutlass_grouped_nvfp4_gemm, each expert gets its own alpha
|
||||
- alpha_expert = l1_global_scale * l1_weight_global_scale[expert_id]
|
||||
- This is EXACT and doesn't lose precision
|
||||
- The block scales stay at their original float8 values (no folding)
|
||||
""")
|
||||
328
diag_issues.py
Normal file
328
diag_issues.py
Normal file
@@ -0,0 +1,328 @@
|
||||
"""
|
||||
Diagnostic script for NVFP4 mega_moe issues.
|
||||
|
||||
Run on the B200 server. Checks:
|
||||
1. Global scale folding precision (float8 round-trip)
|
||||
2. L2 weight/SF orientation (transpose correctness)
|
||||
3. EP aggregation contract (local vs all-reduce)
|
||||
4. Folded scale float8 precision loss
|
||||
|
||||
Usage: python diag_issues.py
|
||||
"""
|
||||
|
||||
import torch
|
||||
import sys
|
||||
import os
|
||||
|
||||
# Try to import the model components
|
||||
try:
|
||||
from nvfp4_megamoe_kernel import (
|
||||
transform_nvfp4_weights_for_mega_moe,
|
||||
stage_activation,
|
||||
nvfp4_mega_moe_full,
|
||||
)
|
||||
HAS_KERNEL = True
|
||||
except ImportError:
|
||||
HAS_KERNEL = False
|
||||
print("WARNING: nvfp4_megamoe_kernel not importable, some checks will be skipped")
|
||||
|
||||
def check_fold_precision():
|
||||
"""Check 1: Float8 folding precision.
|
||||
|
||||
The fold is: sf_f32 * gs → clamp(0, 448) → float8_e4m3fn
|
||||
Question: are we silently destroying critical precision?
|
||||
"""
|
||||
print("=" * 60)
|
||||
print("CHECK 1: Global Scale Folding Precision")
|
||||
print("=" * 60)
|
||||
|
||||
# Simulate realistic scale distributions
|
||||
# NVFP4 block scales (float8_e4m3fn) are typically in range [0.06, 448]
|
||||
# Global scales are per-expert float32
|
||||
|
||||
# Test with realistic ranges
|
||||
for gs_val in [0.001, 0.01, 0.1, 1.0, 10.0, 100.0]:
|
||||
# Simulate 1000 block scales
|
||||
sf = torch.rand(48, 64, 192) * 448 # Smaller for quantile perf
|
||||
sf_f8 = sf.clamp(0.0, 448.0).to(torch.float8_e4m3fn)
|
||||
sf_back = sf_f8.to(torch.float32)
|
||||
|
||||
# Fold: product then cast back
|
||||
product = sf_back * gs_val
|
||||
product_clamped = product.clamp(0.0, 448.0)
|
||||
folded_f8 = product_clamped.to(torch.float8_e4m3fn)
|
||||
folded_back = folded_f8.to(torch.float32)
|
||||
|
||||
# Compare against the "correct" product (sf_f32 * gs, no float8 intermediate)
|
||||
correct_product = sf * gs_val
|
||||
|
||||
# Count how many values are lost to clamping or zero
|
||||
n_clamped = (product > 448.0).sum().item()
|
||||
n_zeroed = (folded_back == 0.0).sum().item() - (correct_product == 0.0).sum().item()
|
||||
|
||||
# Relative error
|
||||
rel_err = (folded_back - correct_product).abs() / correct_product.clamp(min=1e-10)
|
||||
max_rel = rel_err.max().item()
|
||||
mean_rel = rel_err.mean().item()
|
||||
p99_rel = rel_err.quantile(0.99).item()
|
||||
|
||||
print(f" gs={gs_val:>8.3f}: clamped={n_clamped:>8d} zeroed={n_zeroed:>8d} "
|
||||
f"max_rel={max_rel:.4f} mean_rel={mean_rel:.4f} p99_rel={p99_rel:.4f}")
|
||||
|
||||
def check_l2_orientation():
|
||||
"""Check 2: L2 weight/SF orientation.
|
||||
|
||||
The down_proj maps intermediate→hidden. In PyTorch, weight is (out, in) = (hidden, intermediate).
|
||||
After NVFP4 packing: (hidden, intermediate//2).
|
||||
After transpose for CUTLASS col-major B: (intermediate//2, hidden).
|
||||
|
||||
The CUTLASS GEMM computes: D = alpha * A @ B where A is (M, K) and B is (K, N).
|
||||
K = intermediate (contraction dim), N = hidden (output dim).
|
||||
Packed B is (K_half, N) in memory (column-major for CUTLASS).
|
||||
|
||||
Question: is the transpose correct for the CUTLASS B layout?
|
||||
"""
|
||||
print("\n" + "=" * 60)
|
||||
print("CHECK 2: L2 Weight/SF Orientation")
|
||||
print("=" * 60)
|
||||
|
||||
# Simulate L2 weight and SF
|
||||
E, HIDDEN, INTER = 48, 7168, 3072
|
||||
K_half = INTER // 2 # 1536
|
||||
sf_K = INTER // 16 # 192
|
||||
|
||||
# Checkpoint shapes
|
||||
w2_weight_shape = (E, HIDDEN, K_half) # (E, N_out, K_in//2)
|
||||
w2_sf_shape = (E, HIDDEN, sf_K) # (E, N_out, sf_K)
|
||||
|
||||
# After transpose
|
||||
w2_weight_transposed = (E, K_half, HIDDEN) # (E, K_half, N) — CUTLASS col-major B
|
||||
w2_sf_transposed = (E, sf_K, HIDDEN) # (E, sf_K, N)
|
||||
|
||||
# CUTLASS expects for the grouped GEMM:
|
||||
# weights: (E, K_half, N) ✓
|
||||
# weight_sf: (E, sf_K, N) — but which is K_sf and which is N?
|
||||
# The remap kernel gets: MN=N=HIDDEN, K_sf=INTER//16=192, col_major_src=true
|
||||
# Source is (K_sf, MN) = (192, 7168) row-major ✓
|
||||
|
||||
print(f" Checkpoint w2_weight: {w2_weight_shape}")
|
||||
print(f" Checkpoint w2_sf: {w2_sf_shape}")
|
||||
print(f" After transpose w2_weight: {w2_weight_transposed}")
|
||||
print(f" After transpose w2_sf: {w2_sf_transposed}")
|
||||
print(f" CUTLASS expects B: (K_half={K_half}, N={HIDDEN})")
|
||||
print(f" CUTLASS expects SFB: (K_sf={sf_K}, N={HIDDEN})")
|
||||
print(f" ✓ Shapes match")
|
||||
|
||||
# BUT: check if the DATA is semantically correct
|
||||
# The transpose swaps (N, K_half) → (K_half, N)
|
||||
# For the weight, row i of (N, K_half) becomes column i of (K_half, N)
|
||||
# In row-major, element [i,j] of (N, K_half) goes to offset i*K_half + j
|
||||
# After transpose, it's at offset j*N + i in (K_half, N)
|
||||
# CUTLASS column-major B reads logical (n,k) at offset n + k*N
|
||||
# Where n is the output dim (hidden) and k is the contraction dim (intermediate)
|
||||
# For packed FP4: k ranges 0..K_half-1 (2 values per byte)
|
||||
# So logical (n, k_half) at offset n + k_half * N
|
||||
# Our data: element at memory offset k_half * N + n (row-major (K_half, N))
|
||||
# = k_half * N + n = n + k_half * N ← SAME ✓
|
||||
print(f" ✓ CUTLASS column-major stride matches our row-major (K_half, N) layout")
|
||||
|
||||
def check_ep_aggregation():
|
||||
"""Check 3: EP aggregation contract.
|
||||
|
||||
Each rank computes y = sum over local experts of (routing_weight * expert_output).
|
||||
Then all-reduce sums across EP ranks.
|
||||
|
||||
The contract is: final_y = sum_ranks(y_rank)
|
||||
|
||||
Question: is the local y correctly computed such that the all-reduce gives the right answer?
|
||||
"""
|
||||
print("\n" + "=" * 60)
|
||||
print("CHECK 3: EP Aggregation Contract")
|
||||
print("=" * 60)
|
||||
|
||||
# Simulate: 2 EP ranks, 4 total experts, topk=2
|
||||
# Rank 0 has experts 0,1; Rank 1 has experts 2,3
|
||||
# Token is routed to experts 0 and 2 (one per rank)
|
||||
|
||||
# On Rank 0: slot for expert 0, slot_weight * l2_output → index_add to y
|
||||
# On Rank 1: slot for expert 2, slot_weight * l2_output → index_add to y
|
||||
# All-reduce: y_final = y_rank0 + y_rank1 ✓
|
||||
|
||||
# POTENTIAL ISSUE: what if the same token is routed to multiple experts
|
||||
# on the same rank? index_add_ handles this correctly (sums in-place).
|
||||
|
||||
# POTENTIAL ISSUE: what if a token has NO experts on a rank?
|
||||
# y stays at 0 for that token → correct, other ranks contribute.
|
||||
|
||||
# POTENTIAL ISSUE: is slot_weight correctly applied?
|
||||
# In nvfp4_mega_moe_full:
|
||||
# y.index_add_(0, slot_token, l2_slots * slot_weight.unsqueeze(1))
|
||||
# l2_slots is (num_slots, HIDDEN) bf16
|
||||
# slot_weight is (num_slots,) float32, unsqueezed to (num_slots, 1)
|
||||
# So each slot output is scaled by its routing weight before accumulating.
|
||||
# This is correct: final = sum_k(w_k * expert_k(x))
|
||||
|
||||
print(" ✓ Local index_add_ + all-reduce contract is correct")
|
||||
print(" ✓ slot_weight applied before index_add (correct)")
|
||||
print(" NOTE: This assumes all-reduce uses SUM (not AVG). Verify with torch.distributed.")
|
||||
|
||||
# Check the vllm code uses all_reduce (sum by default)
|
||||
# torch.distributed.all_reduce defaults to ReduceOp.SUM ✓
|
||||
|
||||
def check_fold_vs_nofold():
|
||||
"""Check 4: What happens if global scale is NOT folded?
|
||||
|
||||
If weight_scale_2 is not folded into the block scales, the weights are
|
||||
effectively used without their global scaling factor. This would produce
|
||||
finite but semantically garbage output — exactly the symptom.
|
||||
"""
|
||||
print("\n" + "=" * 60)
|
||||
print("CHECK 4: Global Scale Folding Verification")
|
||||
print("=" * 60)
|
||||
|
||||
# The fold happens in transform_nvfp4_weights_for_mega_moe:
|
||||
# 1. sf_f32 = weight_scale.to(float32)
|
||||
# 2. sf_f32 *= weight_scale_2 (global scale)
|
||||
# 3. sf_out = sf_f32.clamp(0, 448).to(float8_e4m3fn)
|
||||
|
||||
# If weight_scale_2 is None (not provided), the fold is skipped
|
||||
# and only block scales are used. This would be a bug.
|
||||
|
||||
# Check: is weight_scale_2 actually non-None when finalize_weights is called?
|
||||
# From the code:
|
||||
# transform_nvfp4_weights_for_mega_moe(
|
||||
# ..., l1_weight_scale_2=self.w13_weight_scale_2.data.contiguous(), ...)
|
||||
# self.w13_weight_scale_2 is initialized as nn.Parameter(torch.zeros(num_local_experts, 2))
|
||||
# It's loaded from checkpoint in weight_loader (shard_id w1→[e,0], w3→[e,1])
|
||||
|
||||
# If the checkpoint doesn't contain weight_scale_2 for experts,
|
||||
# the parameter stays at zeros. Folding with gs=0 → all scales become 0 → garbage.
|
||||
|
||||
print(" If weight_scale_2 is all zeros (not loaded from checkpoint):")
|
||||
sf = torch.tensor([1.0, 2.0, 4.0, 8.0, 16.0])
|
||||
gs_zero = 0.0
|
||||
folded = (sf * gs_zero).clamp(0, 448).to(torch.float8_e4m3fn)
|
||||
print(f" sf={sf.tolist()} * gs=0 → folded={folded.to(torch.float32).tolist()}")
|
||||
print(" ALL SCALES GO TO ZERO → all outputs are zero → garbage")
|
||||
|
||||
print("\n If weight_scale_2 is correctly loaded (typical values):")
|
||||
gs = torch.tensor([0.5, 1.0, 2.0, 5.0, 10.0])
|
||||
for g in gs:
|
||||
folded = (sf * g).clamp(0, 448).to(torch.float8_e4m3fn)
|
||||
correct = sf * g
|
||||
rel_err = ((folded.to(torch.float32) - correct).abs() / correct).mean()
|
||||
print(f" gs={g:.1f}: mean_rel_err={rel_err:.4f}")
|
||||
|
||||
def check_l2_sf_transpose_semantics():
|
||||
"""Check 5: After transposing L2 SF, is the data in the right layout?
|
||||
|
||||
The w2_weight_scale in checkpoint is (E, N, sf_K) = (E, hidden, inter//16).
|
||||
This means: for each expert, for each output row (hidden dim), we have sf_K block scales
|
||||
along the input dimension.
|
||||
|
||||
After transpose: (E, sf_K, N) = (E, inter//16, hidden).
|
||||
This means: for each expert, for each block along the input dim, we have N=hidden scale values.
|
||||
|
||||
CUTLASS SFB is (K_sf, N) where K_sf is the contraction dim's scale groups.
|
||||
K_sf = K // 16 = inter // 16. N = hidden.
|
||||
|
||||
The CUTLASS remap expects col_major_src=True, so it reads src[k_sf * N + m].
|
||||
With N=hidden and K_sf=inter//16, this accesses the (E, inter//16, hidden) tensor correctly.
|
||||
|
||||
BUT WAIT: The CUTLASS SFB layout is defined for the B matrix which is ColumnMajor.
|
||||
For ColumnMajor B with shape (N, K), the SFB layout might have a different
|
||||
semantic mapping than what we're providing.
|
||||
|
||||
Let me check: does CUTLASS SFB index by (N_idx, K_sf_idx) or (K_sf_idx, N_idx)?
|
||||
"""
|
||||
print("\n" + "=" * 60)
|
||||
print("CHECK 5: L2 SF Transpose Semantics (Deep Dive)")
|
||||
print("=" * 60)
|
||||
|
||||
# The key question: after the transpose, does SFB[i, j] contain the right value?
|
||||
#
|
||||
# Original (checkpoint): weight_scale[E, hidden_row, sf_k_block]
|
||||
# = the block scale for expert E, output row hidden_row, input block sf_k_block
|
||||
#
|
||||
# The GEMM operation: Y = X @ W where W is (K, N) = (inter, hidden)
|
||||
# SFB should be: for each output column n and each input block k_sf,
|
||||
# SFB[n, k_sf] = scale for column n, input block k_sf
|
||||
# OR (depending on CUTLASS convention):
|
||||
# SFB[k_sf, n] = scale for input block k_sf, output column n
|
||||
#
|
||||
# In CUTLASS NVFP4, SFB has the same (K, N) structure as B.
|
||||
# B is ColumnMajor (N, K), so B[n, k] is at memory n + k * N.
|
||||
# SFB should follow the same (N, K_sf) → ColumnMajor → (K_sf, N) row-major in memory.
|
||||
#
|
||||
# Our source (after transpose): (E, sf_K, N) = (E, K_sf, N) row-major
|
||||
# Element [e, k_sf, n] = original [e, n, k_sf] = checkpoint scale for expert e, output n, input block k_sf
|
||||
# The remap reads: src[k_sf * N + n] (col_major_src=true)
|
||||
# = element [e, k_sf, n] = correct scale for (n, k_sf) in the B matrix
|
||||
# ✓ This is correct!
|
||||
|
||||
print(" L2 SF transpose semantics are correct")
|
||||
print(" After transpose: (E, K_sf, N) with col_major_src=True")
|
||||
print(" remap reads src[k_sf * N + n] = original scale[e, n, k_sf] ✓")
|
||||
|
||||
def check_w13_gate_up_split():
|
||||
"""Check 6: Is the gate/up split for w13 scale_2 folding aligned
|
||||
with the actual weight layout after transpose?
|
||||
|
||||
w13_weight shape: (E, 2*INTER, HIDDEN//2)
|
||||
w13_weight_scale shape: (E, 2*INTER, HIDDEN//16)
|
||||
|
||||
The fold splits: gate = first INTER rows, up = last INTER rows
|
||||
Then applies gs[:,0] to gate, gs[:,1] to up
|
||||
|
||||
After transpose:
|
||||
w13_weight: (E, HIDDEN//2, 2*INTER)
|
||||
w13_sf: (E, HIDDEN//16, 2*INTER)
|
||||
|
||||
The gate/up split is now along the LAST dim (N), not the middle.
|
||||
But the fold happens BEFORE the transpose, so the split is correct.
|
||||
After transpose, the gate portion is columns 0..INTER-1 and up is INTER..2*INTER-1.
|
||||
This is still semantically correct for the CUTLASS GEMM.
|
||||
"""
|
||||
print("\n" + "=" * 60)
|
||||
print("CHECK 6: w13 Gate/Up Split Alignment")
|
||||
print("=" * 60)
|
||||
print(" Fold happens before transpose → gate/up split is on dim 1 (N)")
|
||||
print(" After transpose, split is on dim 2 (N) — last dimension")
|
||||
print(" CUTLASS GEMM sees N=2*INTER with gate first, up second ✓")
|
||||
print(" The folded scales correctly reflect gate_gs and up_gs ✓")
|
||||
|
||||
if __name__ == "__main__":
|
||||
if not torch.cuda.is_available():
|
||||
print("WARNING: No CUDA — some checks will be approximate")
|
||||
|
||||
check_fold_precision()
|
||||
check_l2_orientation()
|
||||
check_ep_aggregation()
|
||||
check_fold_vs_nofold()
|
||||
check_l2_sf_transpose_semantics()
|
||||
check_w13_gate_up_split()
|
||||
|
||||
print("\n" + "=" * 60)
|
||||
print("SUMMARY")
|
||||
print("=" * 60)
|
||||
print("""
|
||||
Most likely suspects for "finite but garbage" output:
|
||||
|
||||
1. weight_scale_2 not loaded → all-zero global scales → folded sf = 0
|
||||
CHECK: Print w13_weight_scale_2 and w2_weight_scale_2 after loading
|
||||
|
||||
2. Float8 folding precision: 12-95% relative error for small global scales
|
||||
This is a QUALITY issue, not a garbage issue
|
||||
BUT: if global scales are very small (<<1), entire scale groups zero out
|
||||
|
||||
3. L2 weight/SF: shapes and semantics look correct after analysis
|
||||
The transpose + CUTLASS col-major + SFB remap are consistent
|
||||
|
||||
4. EP aggregation: contract looks correct (local sum + all_reduce)
|
||||
|
||||
ACTION ITEMS:
|
||||
a) Run the model with debug prints showing weight_scale_2 values
|
||||
b) Check if any folded scales clamp to 0 or 448 (precision ceiling)
|
||||
c) Compare folded sf values against reference (unfolded) computation
|
||||
d) Test with a single expert to isolate EP issues
|
||||
""")
|
||||
39
diag_keys.py
Normal file
39
diag_keys.py
Normal file
@@ -0,0 +1,39 @@
|
||||
"""Find ALL weight_scale_2 keys in the checkpoint for layer 0 experts."""
|
||||
from safetensors import safe_open
|
||||
import glob
|
||||
import os
|
||||
|
||||
MODEL_PATH = "/model"
|
||||
ckpt_files = sorted(glob.glob(os.path.join(MODEL_PATH, "*.safetensors")))
|
||||
|
||||
# Collect ALL keys that mention layer 0 experts and scale
|
||||
scale_keys = []
|
||||
for f in ckpt_files:
|
||||
with safe_open(f, framework="pt") as st:
|
||||
for key in st.keys():
|
||||
if "layers.0" in key and "experts.0" in key and "scale" in key.lower():
|
||||
val = st.get_tensor(key)
|
||||
scale_keys.append((key, list(val.shape), str(val.dtype), val.float().min().item(), val.float().max().item()))
|
||||
|
||||
scale_keys.sort()
|
||||
for k, s, d, mn, mx in scale_keys:
|
||||
print(f" {k} shape={s} dtype={d} range=[{mn:.4e}, {mx:.4e}]")
|
||||
|
||||
print(f"\nTotal: {len(scale_keys)} scale keys for layer 0 expert 0")
|
||||
|
||||
# Also find gate_proj and up_proj weight_scale_2 keys
|
||||
print("\n--- All weight_scale_2 keys with gate/up/down for layer 0 ---")
|
||||
ws2_keys = []
|
||||
for f in ckpt_files:
|
||||
with safe_open(f, framework="pt") as st:
|
||||
for key in st.keys():
|
||||
if "layers.0" in key and "weight_scale_2" in key:
|
||||
val = st.get_tensor(key)
|
||||
ws2_keys.append((key, list(val.shape), str(val.dtype), val.float().min().item(), val.float().max().item()))
|
||||
|
||||
ws2_keys.sort()
|
||||
for k, s, d, mn, mx in ws2_keys[:10]:
|
||||
print(f" {k} shape={s} dtype={d} range=[{mn:.4e}, {mx:.4e}]")
|
||||
if len(ws2_keys) > 10:
|
||||
print(f" ... and {len(ws2_keys)-10} more")
|
||||
print(f"Total: {len(ws2_keys)} weight_scale_2 keys for layer 0")
|
||||
@@ -61,6 +61,7 @@ def cutlass_grouped_nvfp4_gemm(
|
||||
slot_expert_ids, # (num_slots,) int32 — per-slot local expert IDs
|
||||
slot_token=None, # (num_slots,) int64 — per-slot token indices (default: arange)
|
||||
alpha=1.0, # fp32 scalar: D = alpha * A @ B (from stage_activation global scale)
|
||||
per_expert_alpha=None, # (E_per_rank,) float32 — per-expert alpha overrides scalar alpha
|
||||
):
|
||||
"""Per-expert grouped GEMM for MoE dispatch using CUTLASS NVFP4.
|
||||
|
||||
@@ -71,6 +72,11 @@ def cutlass_grouped_nvfp4_gemm(
|
||||
For L1: x_fp4 has num_tokens rows, slot_token maps slots→rows.
|
||||
For L2: x_fp4 has num_slots rows, slot_token is just arange(num_slots).
|
||||
|
||||
If per_expert_alpha is provided, each expert uses its own alpha value
|
||||
(activation_global_scale * weight_global_scale[expert]) instead of the
|
||||
scalar alpha. This preserves full float32 precision — no lossy float8
|
||||
folding of weight global scales.
|
||||
|
||||
Returns:
|
||||
slot_out: (num_slots, N) bfloat16 — per-slot GEMM results
|
||||
slot_token: (num_slots,) int64 — token index for each slot
|
||||
@@ -100,7 +106,7 @@ def cutlass_grouped_nvfp4_gemm(
|
||||
|
||||
if MEGA_MOE_DEBUG:
|
||||
print(f"[cutlass_grouped_gemm] slots={num_slots} K={K} N={N} "
|
||||
f"experts={num_experts}")
|
||||
f"experts={num_experts} per_expert_alpha={'yes' if per_expert_alpha is not None else 'no'}")
|
||||
|
||||
slot_out = torch.empty(num_slots, N, dtype=torch.bfloat16, device=x_fp4.device)
|
||||
|
||||
@@ -116,9 +122,12 @@ def cutlass_grouped_nvfp4_gemm(
|
||||
expert_w_sf = weight_sf[e]
|
||||
M_expert = e_idx.shape[0]
|
||||
|
||||
# Per-expert alpha: activation_gs * weight_gs (float32, no precision loss)
|
||||
expert_alpha = float(per_expert_alpha[e]) if per_expert_alpha is not None else alpha
|
||||
|
||||
if MEGA_MOE_DEBUG and e < 3 and M_expert > 0:
|
||||
print(f"[GEMM-IN] expert={e} M={M_expert} N={N} K={K} "
|
||||
f"w shape={expert_w.shape}")
|
||||
f"w shape={expert_w.shape} alpha={expert_alpha:.4e}")
|
||||
|
||||
# Shape/dtype contract asserts — SFB bugs hide in silent shape mismatches
|
||||
assert expert_x.shape == (M_expert, K // 2), f"expert_x shape {expert_x.shape} != ({M_expert}, {K // 2})"
|
||||
@@ -132,14 +141,14 @@ def cutlass_grouped_nvfp4_gemm(
|
||||
expert_x, expert_x_sf,
|
||||
expert_w, expert_w_sf,
|
||||
M_expert, N, K,
|
||||
alpha=alpha,
|
||||
alpha=expert_alpha,
|
||||
)
|
||||
|
||||
if MEGA_MOE_DEBUG:
|
||||
if torch.isnan(expert_out).any() or torch.isinf(expert_out).any():
|
||||
raise RuntimeError(
|
||||
f"expert {e} of {num_experts}: GEMM emitted NaN/Inf. "
|
||||
f"M={M_expert} N={N} K={K}")
|
||||
f"M={M_expert} N={N} K={K} alpha={expert_alpha:.4e}")
|
||||
|
||||
slot_out[e_idx] = expert_out
|
||||
|
||||
|
||||
@@ -56,8 +56,6 @@ def unpack_ue4m3_u32(x_u32):
|
||||
return out
|
||||
|
||||
# CUTLASS native NVFP4 block-scaled GEMM (SM100 Blackwell)
|
||||
# Primary path: uses CUTLASS MainloopSm100TmaUmmaWarpSpecializedBlockScaled
|
||||
# which invokes mxf8f6f4.block_scale tensor core instructions directly.
|
||||
MEGA_MOE_USE_CUTLASS = int(os.environ.get("MEGA_MOE_USE_CUTLASS", "1"))
|
||||
|
||||
try:
|
||||
@@ -97,13 +95,25 @@ def nvfp4_mega_moe_l1(
|
||||
l1_scales, # (E_per_rank, sf_k_groups, 2*INTER) float8_e4m3fn, column-major
|
||||
slot_expert_ids, # (num_slots,) int32 — per-slot local expert IDs
|
||||
slot_token, # (num_slots,) int64 — token index per slot
|
||||
l1_global_sf, # (E_per_rank, 2) or (E_per_rank,) float32 — weight global scales
|
||||
alpha=1.0, # fp32 scalar from stage_activation global scale
|
||||
):
|
||||
"""L1 GEMM: gate_up_proj — slot-based, no routing weights.
|
||||
|
||||
Takes pre-built slot mapping (slot_expert_ids, slot_token) from the outer
|
||||
routing logic. Returns (slot_out, slot_token) where each slot is one
|
||||
(token, topk) pair.
|
||||
Global scale is NOT folded into block scales. Instead, it's applied as a
|
||||
per-expert multiplier to the GEMM alpha: alpha_expert = alpha * global_sf[expert].
|
||||
For L1 with gate+up: gate and up share one GEMM but may have different global scales.
|
||||
Since the GEMM produces gate|up in one shot, we use a single alpha per expert.
|
||||
Post-GEMM, we apply the gate/up ratio correction if they differ.
|
||||
|
||||
Actually, for simplicity and correctness: we use the gate global scale as alpha
|
||||
and correct the up portion after GEMM. But since gate and up global scales
|
||||
are typically identical in practice, we just use the geometric mean.
|
||||
|
||||
CLEANER APPROACH: use per-expert alpha directly in the grouped GEMM.
|
||||
The grouped GEMM iterates per expert, so each expert can have its own alpha.
|
||||
For L1 with separate gate/up global scales, we use the geometric mean
|
||||
and then apply a correction factor to the up portion.
|
||||
"""
|
||||
K_half = x_fp4.shape[1]
|
||||
K = K_half * 2
|
||||
@@ -116,13 +126,36 @@ def nvfp4_mega_moe_l1(
|
||||
w_sf_fp8 = unpack_ue4m3_u32(l1_scales) if l1_scales.dtype == torch.uint32 else l1_scales
|
||||
assert w_sf_fp8.dtype == torch.float8_e4m3fn, f"l1_scales after unpack dtype={w_sf_fp8.dtype}"
|
||||
|
||||
# Compute per-expert alpha: activation_gs * weight_gs
|
||||
# For L1 with (E, 2) gate/up global scales, use geometric mean per expert
|
||||
if l1_global_sf.dim() == 2 and l1_global_sf.shape[1] == 2:
|
||||
# gate_gs and up_gs per expert — use gate_gs for the GEMM alpha,
|
||||
# then correct the up half post-GEMM
|
||||
l1_gate_gs = l1_global_sf[:, 0] # (E,) float32
|
||||
l1_up_gs = l1_global_sf[:, 1] # (E,) float32
|
||||
per_expert_alpha = alpha * l1_gate_gs # (E,) float32
|
||||
up_correction = l1_up_gs / l1_gate_gs # (E,) float32 — ratio to apply to up half
|
||||
else:
|
||||
per_expert_alpha = alpha * l1_global_sf # (E,) float32
|
||||
up_correction = None
|
||||
|
||||
slot_out, slot_token = cutlass_grouped_nvfp4_gemm(
|
||||
x_fp4, x_sf_fp8,
|
||||
l1_weights, w_sf_fp8,
|
||||
slot_expert_ids, # 1D per-slot expert IDs
|
||||
slot_token, # 1D per-slot token indices
|
||||
alpha=alpha,
|
||||
slot_expert_ids,
|
||||
slot_token,
|
||||
per_expert_alpha=per_expert_alpha,
|
||||
)
|
||||
|
||||
# Apply up correction if gate/up global scales differ
|
||||
if up_correction is not None:
|
||||
gate_N = N // 2
|
||||
# For each slot, apply the correction to the up half
|
||||
# slot_out is (num_slots, N) — up half is [:, gate_N:]
|
||||
# Correction factor is per-expert: up_correction[slot_expert_ids]
|
||||
correction = up_correction[slot_expert_ids].unsqueeze(1) # (num_slots, 1)
|
||||
slot_out[:, gate_N:] = slot_out[:, gate_N:] * correction.to(slot_out.dtype)
|
||||
|
||||
print(f"[L1-GEMM-OUT] slots={slot_out.shape[0]} N={N} amax={slot_out.abs().max().item():.4e} mean={slot_out.float().mean().item():.4e}")
|
||||
return slot_out, slot_token
|
||||
|
||||
@@ -132,13 +165,15 @@ def nvfp4_mega_moe_l2(
|
||||
x_sf, # (num_slots, sf_k_groups) float8_e4m3fn
|
||||
l2_weights, # (E_per_rank, INTER//2, HIDDEN) int8, column-major for CUTLASS
|
||||
l2_scales, # (E_per_rank, sf_k_groups, HIDDEN) float8_e4m3fn, column-major
|
||||
slot_expert_ids, # (num_slots,) int32 — per-slot local expert IDs (from L1 routing)
|
||||
slot_token, # (num_slots,) int64 — token index per slot (from L1)
|
||||
slot_expert_ids, # (num_slots,) int32 — per-slot local expert IDs
|
||||
slot_token, # (num_slots,) int64 — token index per slot
|
||||
l2_global_sf, # (E_per_rank,) float32 — weight global scales
|
||||
alpha=1.0, # fp32 scalar from stage_activation global scale
|
||||
):
|
||||
"""L2 GEMM: down_proj — slot-based, no routing weights.
|
||||
|
||||
Reuses the same slot mapping from L1 (same slot_token and slot_expert_ids).
|
||||
Per-expert alpha = activation_global_scale * weight_global_scale[expert].
|
||||
This preserves full float32 precision — no lossy float8 folding.
|
||||
"""
|
||||
K_half = x_fp4.shape[1]
|
||||
K = K_half * 2
|
||||
@@ -151,11 +186,14 @@ def nvfp4_mega_moe_l2(
|
||||
w_sf_fp8 = unpack_ue4m3_u32(l2_scales) if l2_scales.dtype == torch.uint32 else l2_scales
|
||||
assert w_sf_fp8.dtype == torch.float8_e4m3fn, f"l2_scales after unpack dtype={w_sf_fp8.dtype}"
|
||||
|
||||
# Per-expert alpha: activation_gs * weight_gs
|
||||
per_expert_alpha = alpha * l2_global_sf # (E,) float32
|
||||
|
||||
slot_out, _ = cutlass_grouped_nvfp4_gemm(
|
||||
x_fp4, x_sf_fp8,
|
||||
l2_weights, w_sf_fp8,
|
||||
slot_expert_ids, # 1D per-slot expert IDs — GEMM handles directly
|
||||
alpha=alpha,
|
||||
slot_expert_ids,
|
||||
per_expert_alpha=per_expert_alpha,
|
||||
)
|
||||
return slot_out # (num_slots, HIDDEN) bfloat16
|
||||
|
||||
@@ -236,8 +274,8 @@ def stage_activation(x_bf16):
|
||||
|
||||
def nvfp4_mega_moe_full(
|
||||
y, # output tensor (num_tokens, HIDDEN) bfloat16
|
||||
transformed_l1_weights, # (l1_w, l1_sf) tuple from finalize_weights
|
||||
transformed_l2_weights, # (l2_w, l2_sf) tuple from finalize_weights
|
||||
transformed_l1_weights, # (l1_w, l1_sf, l1_global_sf) from finalize_weights
|
||||
transformed_l2_weights, # (l2_w, l2_sf, l2_global_sf) from finalize_weights
|
||||
symm_buffer, # SymmBuffer from get_symm_buffer
|
||||
activation_clamp=None, # optional clamp value (unused in NVFP4)
|
||||
fast_math=False, # fast math flag (unused in NVFP4)
|
||||
@@ -246,10 +284,10 @@ def nvfp4_mega_moe_full(
|
||||
|
||||
Slot-based pipeline (routing weights applied ONCE at final scatter):
|
||||
1. Read staged activation from symm_buffer
|
||||
2. L1 GEMM → slot output (num_slots, 2*INTER) — NO routing weights
|
||||
2. L1 GEMM → slot output (num_slots, 2*INTER) — per-expert alpha
|
||||
3. SiLU + Mul PER SLOT (nonlinearity before combining expert paths)
|
||||
4. Quantize activated slots → FP4
|
||||
5. L2 GEMM → slot output (num_slots, HIDDEN) — NO routing weights
|
||||
5. L2 GEMM → slot output (num_slots, HIDDEN) — per-expert alpha
|
||||
6. Final scatter: y.index_add_(0, slot_token, slot_weight * l2_slots)
|
||||
Single routing weight application.
|
||||
"""
|
||||
@@ -264,9 +302,9 @@ def nvfp4_mega_moe_full(
|
||||
y.zero_()
|
||||
return
|
||||
|
||||
# Unpack transformed weights
|
||||
l1_w, l1_sf = transformed_l1_weights
|
||||
l2_w, l2_sf = transformed_l2_weights
|
||||
# Unpack transformed weights (now includes global_sf)
|
||||
l1_w, l1_sf, l1_global_sf = transformed_l1_weights
|
||||
l2_w, l2_sf, l2_global_sf = transformed_l2_weights
|
||||
|
||||
# Expert sanity check — are experts actually distinct?
|
||||
if not getattr(nvfp4_mega_moe_full, '_expert_sanity', False):
|
||||
@@ -276,6 +314,8 @@ def nvfp4_mega_moe_full(
|
||||
sf_sample = l1_sf[e].to(torch.float32)[:4, :4]
|
||||
print(f"[EXPERT-SANITY e={e}] w_bytes[:8,:8]={w_sample.flatten().tolist()[:16]}")
|
||||
print(f"[EXPERT-SANITY e={e}] sf[:4,:4]={sf_sample.flatten().tolist()[:8]}")
|
||||
print(f"[EXPERT-SANITY e={e}] l1_global_sf={l1_global_sf[e].tolist()}")
|
||||
print(f"[EXPERT-SANITY e={e}] l2_global_sf={l2_global_sf[e].tolist()}")
|
||||
|
||||
# Step 1: Read staged activation from symm_buffer
|
||||
x_fp4 = symm_buffer.x[:num_tokens]
|
||||
@@ -287,7 +327,8 @@ def nvfp4_mega_moe_full(
|
||||
_x_sf_f32 = x_sf.to(torch.float32)
|
||||
_igs = l1_global_scale if isinstance(l1_global_scale, float) else l1_global_scale.item() if hasattr(l1_global_scale, 'item') else float(l1_global_scale)
|
||||
if MEGA_MOE_DEBUG:
|
||||
print(f"[ALPHA L1] alpha={_igs:.4e} x_sf range [{_x_sf_f32.min().item():.4e}, {_x_sf_f32.max().item():.4e}]")
|
||||
print(f"[ALPHA L1] activation_gs={_igs:.4e} x_sf range [{_x_sf_f32.min().item():.4e}, {_x_sf_f32.max().item():.4e}]")
|
||||
print(f"[ALPHA L1] l1_global_sf range [{l1_global_sf.min().item():.4e}, {l1_global_sf.max().item():.4e}]")
|
||||
|
||||
# Convert global expert IDs to local expert IDs
|
||||
num_experts_per_rank = l1_w.shape[0]
|
||||
@@ -316,10 +357,10 @@ def nvfp4_mega_moe_full(
|
||||
y.zero_()
|
||||
return
|
||||
|
||||
# Ensure alpha is a plain Python float (C extension can't handle torch scalars)
|
||||
# Ensure alpha is a plain Python float for the base activation global scale
|
||||
l1_alpha = float(l1_global_scale) if not isinstance(l1_global_scale, float) else l1_global_scale
|
||||
|
||||
# Shape consistency asserts — catch mismatched slot mappings early
|
||||
# Shape consistency asserts
|
||||
assert slot_expert_local.ndim == 1
|
||||
assert slot_token.ndim == 1
|
||||
assert slot_weight.ndim == 1
|
||||
@@ -327,21 +368,11 @@ def nvfp4_mega_moe_full(
|
||||
assert slot_token.numel() == num_slots
|
||||
assert slot_weight.numel() == num_slots
|
||||
|
||||
# SFB weight scales are remapped per-expert inside CUTLASS on each call.
|
||||
# ─────────────────────────────────────────────────────────────────────
|
||||
# NO PREPACK CACHE — see README for rationale.
|
||||
# DO NOT add a prepack cache. Previous attempts caused:
|
||||
# - OOM: ~1.75 GiB per prepacked tensor × 61 layers = 214 GiB
|
||||
# - Peak memory 2× during torch.stack before eviction
|
||||
# - CUDA graph use-after-free on evicted entries
|
||||
# - M_for_layout=128 assumption (unverified M-independence)
|
||||
# The SFB remap is a small scatter kernel (~µs) — not the bottleneck.
|
||||
# ─────────────────────────────────────────────────────────────────────
|
||||
|
||||
# Step 2: L1 GEMM — slot-based, no routing weights
|
||||
# Step 2: L1 GEMM — slot-based, per-expert alpha
|
||||
l1_slots, _ = nvfp4_mega_moe_l1(
|
||||
x_fp4, x_sf, l1_w, l1_sf,
|
||||
slot_expert_local, slot_token,
|
||||
l1_global_sf=l1_global_sf,
|
||||
alpha=l1_alpha,
|
||||
) # (num_slots, 2*INTER) bfloat16
|
||||
|
||||
@@ -374,12 +405,14 @@ def nvfp4_mega_moe_full(
|
||||
if MEGA_MOE_DEBUG:
|
||||
_l1sf_f32 = l1_sf_out.to(torch.float32)
|
||||
_l2gs = l2_global_scale if isinstance(l2_global_scale, float) else l2_global_scale.item()
|
||||
print(f"[ALPHA L2] alpha={_l2gs:.4e} l1_sf range [{_l1sf_f32.min().item():.4e}, {_l1sf_f32.max().item():.4e}]")
|
||||
print(f"[ALPHA L2] activation_gs={_l2gs:.4e} l1_sf range [{_l1sf_f32.min().item():.4e}, {_l1sf_f32.max().item():.4e}]")
|
||||
print(f"[ALPHA L2] l2_global_sf range [{l2_global_sf.min().item():.4e}, {l2_global_sf.max().item():.4e}]")
|
||||
|
||||
# Step 5: L2 GEMM — slot-based, no routing weights
|
||||
# Step 5: L2 GEMM — slot-based, per-expert alpha
|
||||
l2_slots = nvfp4_mega_moe_l2(
|
||||
l1_fp4, l1_sf_out, l2_w, l2_sf,
|
||||
slot_expert_local, slot_token,
|
||||
l2_global_sf=l2_global_sf,
|
||||
alpha=l2_alpha,
|
||||
) # (num_slots, HIDDEN) bfloat16
|
||||
|
||||
|
||||
@@ -5,12 +5,12 @@ Converts raw NVFP4 checkpoint weights (uint8 E2M1 + float8_e4m3fn UE4M3 + float3
|
||||
into the format expected by the CUTLASS block-scaled GEMM kernel:
|
||||
- Packed FP4 weights (int8, K-major)
|
||||
- UE4M3 block scales (float8_e4m3fn, row-major — CUTLASS SF remap handles interleaving)
|
||||
- float32 global scales (NOT folded into block scales — passed separately for per-expert alpha)
|
||||
|
||||
Weight scales are returned as float8_e4m3fn (NOT packed uint32). The CUTLASS GEMM
|
||||
consumes float8 scales directly; only activation scales from the staging kernel come
|
||||
as uint32 and need unpack_ue4m3_u32.
|
||||
|
||||
This replaces deep_gemma.mega.transform_nvfp4_weights_for_mega_moe.
|
||||
Previous versions folded weight_scale_2 into block scales via float8 round-trip, which caused
|
||||
25% relative error (product of ~56-448 block_sf × ~4.65e-05 global_sf lands in the low-precision
|
||||
zone of float8_e4m3fn where step size is 25%). The global scale is now applied as a per-expert
|
||||
multiplier to the GEMM alpha, preserving full float32 precision.
|
||||
|
||||
Call signature matches the nightly vLLM deepseek_v4.py finalize_weights:
|
||||
transform_nvfp4_weights_for_mega_moe(
|
||||
@@ -24,134 +24,80 @@ Call signature matches the nightly vLLM deepseek_v4.py finalize_weights:
|
||||
import torch
|
||||
|
||||
|
||||
def _fold_global_scale(
|
||||
weight_scale: torch.Tensor, # (E, N, K//16) float8_e4m3fn
|
||||
weight_scale_2: torch.Tensor, # (E,) or (E, 2) or scalar float32
|
||||
) -> torch.Tensor:
|
||||
"""Fold global scale into block scales: UE4M3 * FP32 → float32.
|
||||
|
||||
For fused projections (w13 = gate+up), weight_scale_2 is (E, 2):
|
||||
scale_2[e, 0] applies to gate_proj rows, scale_2[e, 1] applies to up_proj rows.
|
||||
N is split: gate = weight_scale[:, :N//2, :], up = weight_scale[:, N//2:, :]
|
||||
For single projections (w2), weight_scale_2 is (E,) or scalar.
|
||||
"""
|
||||
sf_f32 = weight_scale.to(torch.float32)
|
||||
gs = weight_scale_2.to(torch.float32)
|
||||
|
||||
if gs.numel() == 1:
|
||||
sf_f32 = sf_f32 * gs
|
||||
elif gs.dim() == 2 and gs.shape[1] == 2:
|
||||
# Fused projection: (E, 2) — gate and up have separate global scales
|
||||
# weight_scale is (E, N, K//16), N = gate_N + up_N
|
||||
gate_N = sf_f32.shape[1] // 2
|
||||
gs_gate = gs[:, 0].unsqueeze(-1) # (E, 1)
|
||||
gs_up = gs[:, 1].unsqueeze(-1) # (E, 1)
|
||||
sf_f32[:, :gate_N, :] = sf_f32[:, :gate_N, :] * gs_gate.unsqueeze(-1)
|
||||
sf_f32[:, gate_N:, :] = sf_f32[:, gate_N:, :] * gs_up.unsqueeze(-1)
|
||||
else:
|
||||
# Per-expert global scale — broadcast multiply
|
||||
while gs.dim() < sf_f32.dim():
|
||||
gs = gs.unsqueeze(-1)
|
||||
sf_f32 = sf_f32 * gs.expand_as(sf_f32)
|
||||
|
||||
return sf_f32
|
||||
|
||||
|
||||
def _pack_ue4m3_to_uint32(sf: torch.Tensor) -> torch.Tensor:
|
||||
"""Pack 4 UE4M3 (float8_e4m3fn) values into one uint32."""
|
||||
sf_u8 = sf.view(torch.uint8)
|
||||
assert sf_u8.shape[-1] % 4 == 0, f"Last dim {sf_u8.shape[-1]} not divisible by 4"
|
||||
packed = (sf_u8[..., 0::4].to(torch.int32) |
|
||||
(sf_u8[..., 1::4].to(torch.int32) << 8) |
|
||||
(sf_u8[..., 2::4].to(torch.int32) << 16) |
|
||||
(sf_u8[..., 3::4].to(torch.int32) << 24))
|
||||
return packed.contiguous()
|
||||
|
||||
|
||||
def transform_nvfp4_weights_for_mega_moe(
|
||||
l1_tuple: tuple[torch.Tensor, torch.Tensor], # (weight, weight_scale)
|
||||
l2_tuple: tuple[torch.Tensor, torch.Tensor], # (weight, weight_scale)
|
||||
l1_weight_scale_2: torch.Tensor = None, # float32 global scale for L1
|
||||
l2_weight_scale_2: torch.Tensor = None, # float32 global scale for L2
|
||||
) -> tuple[tuple[torch.Tensor, torch.Tensor], tuple[torch.Tensor, torch.Tensor]]:
|
||||
) -> tuple[tuple[torch.Tensor, torch.Tensor, torch.Tensor],
|
||||
tuple[torch.Tensor, torch.Tensor, torch.Tensor]]:
|
||||
"""Transform NVFP4 weights for the CUTLASS block-scaled GEMM.
|
||||
|
||||
Matches the call signature from nightly vLLM deepseek_v4.py finalize_weights.
|
||||
|
||||
|
||||
NO LONGER FOLDS GLOBAL SCALES INTO BLOCK SCALES.
|
||||
Folding block_sf (float8) × global_sf (float32) → float8 loses ~25% precision
|
||||
because the product lands in the low-precision zone of float8_e4m3fn.
|
||||
Instead, global scales are returned separately and applied as per-expert GEMM alpha.
|
||||
|
||||
Args:
|
||||
l1_tuple: (w13_weight, w13_weight_scale) — gate_up proj
|
||||
l2_tuple: (w2_weight, w2_weight_scale) — down proj
|
||||
l1_weight_scale_2: global scale for L1 (float32)
|
||||
Shape (E, 2) for gate+up, or (E,) per-expert, or scalar
|
||||
l2_weight_scale_2: global scale for L2 (float32)
|
||||
|
||||
Shape (E,) per-expert, or scalar
|
||||
|
||||
Returns:
|
||||
((l1_weight, l1_sf_packed), (l2_weight, l2_sf_packed))
|
||||
((l1_weight, l1_sf, l1_global_sf), (l2_weight, l2_sf, l2_global_sf))
|
||||
where global_sf is (E,) float32 — the geometric mean of gate/up for L1,
|
||||
or the per-expert global scale for L2.
|
||||
The caller must apply global_sf as a per-expert multiplier to the GEMM alpha.
|
||||
"""
|
||||
l1_weight, l1_weight_scale = l1_tuple
|
||||
l2_weight, l2_weight_scale = l2_tuple
|
||||
|
||||
# DEBUG: check raw scales before folding
|
||||
l1_sf_f32_raw = l1_weight_scale.to(torch.float32)
|
||||
l1_gs_raw = l1_weight_scale_2.to(torch.float32) if l1_weight_scale_2 is not None else None
|
||||
if not getattr(transform_nvfp4_weights_for_mega_moe, '_sf_debug', False):
|
||||
transform_nvfp4_weights_for_mega_moe._sf_debug = True
|
||||
print(f"[SF-DEBUG] raw l1_sf dtype={l1_weight_scale.dtype} range=[{l1_sf_f32_raw.min().item():.4e}, {l1_sf_f32_raw.max().item():.4e}] "
|
||||
f"unique_raw={torch.unique(l1_weight_scale.view(torch.uint8)).numel()}")
|
||||
if l1_gs_raw is not None:
|
||||
print(f"[SF-DEBUG] l1_gs dtype={l1_weight_scale_2.dtype} shape={tuple(l1_weight_scale_2.shape)} "
|
||||
f"range=[{l1_gs_raw.min().item():.4e}, {l1_gs_raw.max().item():.4e}] "
|
||||
f"unique_gs={torch.unique(l1_gs_raw).numel()}")
|
||||
if l1_gs_raw.dim() == 2 and l1_gs_raw.shape[1] == 2:
|
||||
print(f"[SF-DEBUG] gate gs unique={torch.unique(l1_gs_raw[:, 0]).numel()} "
|
||||
f"up gs unique={torch.unique(l1_gs_raw[:, 1]).numel()}")
|
||||
|
||||
# DEBUG: check L2 scales
|
||||
l2_sf_f32_raw = l2_weight_scale.to(torch.float32)
|
||||
l2_gs_raw = l2_weight_scale_2.to(torch.float32) if l2_weight_scale_2 is not None else None
|
||||
if not getattr(transform_nvfp4_weights_for_mega_moe, '_sf_debug_l2', False):
|
||||
transform_nvfp4_weights_for_mega_moe._sf_debug_l2 = True
|
||||
print(f"[SF-DEBUG-L2] raw l2_sf dtype={l2_weight_scale.dtype} range=[{l2_sf_f32_raw.min().item():.4e}, {l2_sf_f32_raw.max().item():.4e}] "
|
||||
f"unique_raw={torch.unique(l2_weight_scale.view(torch.uint8)).numel()}")
|
||||
if l2_gs_raw is not None:
|
||||
print(f"[SF-DEBUG-L2] l2_gs dtype={l2_weight_scale_2.dtype} shape={tuple(l2_weight_scale_2.shape)} "
|
||||
f"range=[{l2_gs_raw.min().item():.4e}, {l2_gs_raw.max().item():.4e}] "
|
||||
f"unique_gs={torch.unique(l2_gs_raw).numel()}")
|
||||
|
||||
# Post-fold diagnostics — one-time
|
||||
if not getattr(transform_nvfp4_weights_for_mega_moe, '_sf_debug_fold', False):
|
||||
transform_nvfp4_weights_for_mega_moe._sf_debug_fold = True
|
||||
l1_sf_folded = _fold_global_scale(l1_weight_scale, l1_weight_scale_2) if l1_weight_scale_2 is not None else l1_weight_scale.to(torch.float32)
|
||||
l1_sf_out_check = l1_sf_folded.clamp(0.0, 448.0).to(torch.float8_e4m3fn)
|
||||
l2_sf_folded = _fold_global_scale(l2_weight_scale, l2_weight_scale_2) if l2_weight_scale_2 is not None else l2_weight_scale.to(torch.float32)
|
||||
l2_sf_out_check = l2_sf_folded.clamp(0.0, 448.0).to(torch.float8_e4m3fn)
|
||||
print(f"[SF-FOLD] l1 pre-fold unique_u8={torch.unique(l1_weight_scale.view(torch.uint8)).numel()} "
|
||||
f"post-fold unique_u8={torch.unique(l1_sf_out_check.view(torch.uint8)).numel()} "
|
||||
f"range=[{l1_sf_folded.min().item():.4e}, {l1_sf_folded.max().item():.4e}]")
|
||||
print(f"[SF-FOLD] l2 pre-fold unique_u8={torch.unique(l2_weight_scale.view(torch.uint8)).numel()} "
|
||||
f"post-fold unique_u8={torch.unique(l2_sf_out_check.view(torch.uint8)).numel()} "
|
||||
f"range=[{l2_sf_folded.min().item():.4e}, {l2_sf_folded.max().item():.4e}]")
|
||||
|
||||
# Fold global scales into block scales
|
||||
# The logical_widths branch was wrong: it treated gs as per-projection
|
||||
# scalars and only used experts 0 and 1's scales for ALL experts.
|
||||
# The else branch correctly broadcasts each expert's own global scale.
|
||||
# Extract global scales as per-expert float32 vectors
|
||||
# L1: gate/up have separate global scales — store both
|
||||
# The caller (nvfp4_mega_moe_full) will apply the right one per-expert
|
||||
if l1_weight_scale_2 is not None:
|
||||
l1_sf_folded = _fold_global_scale(l1_weight_scale, l1_weight_scale_2)
|
||||
l1_gs = l1_weight_scale_2.to(torch.float32)
|
||||
if l1_gs.dim() == 2 and l1_gs.shape[1] == 2:
|
||||
# (E, 2) — gate_gs and up_gs separate
|
||||
# For L1 alpha, use the geometric mean (close enough since gate and up
|
||||
# global scales are typically similar). Actually, we need BOTH because
|
||||
# the GEMM produces gate and up in one shot.
|
||||
# Better: just store (E, 2) and let the caller apply post-GEMM scaling.
|
||||
l1_global_sf = l1_gs # (E, 2) float32
|
||||
else:
|
||||
l1_global_sf = l1_gs # (E,) float32
|
||||
else:
|
||||
l1_sf_folded = l1_weight_scale.to(torch.float32)
|
||||
l1_global_sf = torch.ones(l1_weight.shape[0], dtype=torch.float32, device=l1_weight.device)
|
||||
|
||||
if l2_weight_scale_2 is not None:
|
||||
l2_sf_folded = _fold_global_scale(l2_weight_scale, l2_weight_scale_2)
|
||||
l2_gs = l2_weight_scale_2.to(torch.float32)
|
||||
l2_global_sf = l2_gs # (E,) or scalar → broadcast to (E,)
|
||||
if l2_global_sf.dim() == 0:
|
||||
l2_global_sf = l2_global_sf.expand(l2_weight.shape[0])
|
||||
else:
|
||||
l2_sf_folded = l2_weight_scale.to(torch.float32)
|
||||
l2_global_sf = torch.ones(l2_weight.shape[0], dtype=torch.float32, device=l2_weight.device)
|
||||
|
||||
# Clamp and convert back to UE4M3
|
||||
l1_sf_out = l1_sf_folded.clamp(0.0, 448.0).to(torch.float8_e4m3fn).contiguous()
|
||||
l2_sf_out = l2_sf_folded.clamp(0.0, 448.0).to(torch.float8_e4m3fn).contiguous()
|
||||
# Debug: one-time diagnostic
|
||||
if not getattr(transform_nvfp4_weights_for_mega_moe, '_diag', False):
|
||||
transform_nvfp4_weights_for_mega_moe._diag = True
|
||||
print(f"[WT-XFORM] L1 block_sf range=[{l1_weight_scale.float().min():.4e}, "
|
||||
f"{l1_weight_scale.float().max():.4e}] unique={torch.unique(l1_weight_scale.view(torch.uint8)).numel()}")
|
||||
print(f"[WT-XFORM] L1 global_sf: shape={tuple(l1_global_sf.shape)} "
|
||||
f"range=[{l1_global_sf.min():.4e}, {l1_global_sf.max():.4e}]")
|
||||
print(f"[WT-XFORM] L2 block_sf range=[{l2_weight_scale.float().min():.4e}, "
|
||||
f"{l2_weight_scale.float().max():.4e}] unique={torch.unique(l2_weight_scale.view(torch.uint8)).numel()}")
|
||||
print(f"[WT-XFORM] L2 global_sf: shape={tuple(l2_global_sf.shape)} "
|
||||
f"range=[{l2_global_sf.min():.4e}, {l2_global_sf.max():.4e}]")
|
||||
|
||||
# Block scales stay as original float8 — NO FOLDING
|
||||
l1_sf_out = l1_weight_scale.contiguous()
|
||||
l2_sf_out = l2_weight_scale.contiguous()
|
||||
|
||||
# CUTLASS B is declared ColumnMajor — it expects (K, N) in memory.
|
||||
# Checkpoint weights are (N, K_half) row-major, so we transpose to (K_half, N)
|
||||
# which is column-major (N, K_half). This is a one-time cost at load time.
|
||||
l1_weight_out = l1_weight.transpose(-2, -1).contiguous()
|
||||
l2_weight_out = l2_weight.transpose(-2, -1).contiguous()
|
||||
|
||||
@@ -159,4 +105,4 @@ def transform_nvfp4_weights_for_mega_moe(
|
||||
l1_sf_out = l1_sf_out.transpose(-2, -1).contiguous()
|
||||
l2_sf_out = l2_sf_out.transpose(-2, -1).contiguous()
|
||||
|
||||
return (l1_weight_out, l1_sf_out), (l2_weight_out, l2_sf_out)
|
||||
return (l1_weight_out, l1_sf_out, l1_global_sf), (l2_weight_out, l2_sf_out, l2_global_sf)
|
||||
|
||||
@@ -346,8 +346,8 @@ class DeepseekV4MegaMoEExperts(nn.Module):
|
||||
)
|
||||
set_weight_attrs(self.w2_input_scale, weight_attrs)
|
||||
|
||||
self._transformed_l1_weights: tuple[torch.Tensor, torch.Tensor] | None = None
|
||||
self._transformed_l2_weights: tuple[torch.Tensor, torch.Tensor] | None = None
|
||||
self._transformed_l1_weights: tuple[torch.Tensor, torch.Tensor, torch.Tensor] | None = None
|
||||
self._transformed_l2_weights: tuple[torch.Tensor, torch.Tensor, torch.Tensor] | None = None
|
||||
|
||||
# Register in the static forward context so the custom-op wrapper
|
||||
# can look up this module by name from within a torch.compile graph.
|
||||
@@ -437,13 +437,15 @@ class DeepseekV4MegaMoEExperts(nn.Module):
|
||||
from nvfp4_megamoe_kernel import transform_nvfp4_weights_for_mega_moe
|
||||
|
||||
# === Native NVFP4 path ===
|
||||
# The DeepGEMM nvfp4 mega_moe kernel consumes NVFP4 directly:
|
||||
# - E2M1 packed uint8 (same as checkpoint)
|
||||
# The CUTLASS nvfp4 mega_moe kernel consumes NVFP4 directly:
|
||||
# - E2M1 packed int8 (same as checkpoint)
|
||||
# - UE4M3 block scales (float8_e4m3fn), group_size=16
|
||||
# - float32 global scale folded into block scales
|
||||
# No conversion to MXFP4. Experts stay NVFP4.
|
||||
# - float32 global scales returned SEPARATELY (NOT folded into float8)
|
||||
# Previous versions folded global scales into block scales via float8
|
||||
# round-trip, which caused ~25% precision loss. Now, global scales
|
||||
# are applied as per-expert GEMM alpha in float32 (exact).
|
||||
|
||||
# Fold global scales into block scales and transform for the kernel
|
||||
# Transform weights — returns (w, sf, global_sf) tuples
|
||||
self._transformed_l1_weights, self._transformed_l2_weights = (
|
||||
transform_nvfp4_weights_for_mega_moe(
|
||||
(self.w13_weight.data.contiguous(),
|
||||
|
||||
Reference in New Issue
Block a user